People are able to easily discriminate the movement of humans from other kinds of movement. This ability is sometimes referred to as `biological motion perception', and it has been the focus of a great deal of scientific interest due to the important role it plays in human behavior and social interactions. Although a great deal of research has been carried out to explore various aspects of how we are able to detect and recognize human movement, there has been little or no research that has tried to quantify how much information is carried by the motion of the human body nor how much of this information a human observer is able to use when trying to detect, discrimination or identify human motion. Ideal observer analysis is a technique that allows one to quantitatively answer these questions. Our preliminary results have shown that, contrary to current dogma, human observers make very poor use of information carried by the stimuli and tasks typically used in biological motion experiments. The first set of experiments described in this proposal are designed to build on these results by using a signal-detection based model of visual information processing and a series of system identification techniques to specify the mechanisms that mediate the perception of biological motion. The finding that information use in biological motion perception tasks is so poor has led us to a second, related line of research. The poor use of information we have observed in biological motion perception tasks is actually not uncommon for tasks that involve the perception of complex objects, such as faces and geometric forms. This has led use to ask the question: why is information processing so poor in these tasks? It is our hypothesis that the cause of inefficient information use in these tasks is due to the highly artificial and constrained conditions typically used in psychophysical experiments. Rather, the human visual system has evolved to be robust under conditions of stimulus variability and uncertainty. In this proposal, we outline a new approach called `task-based ideal observer analysis'(TBIOA) that involves the systematic manipulation of a task in order to maximize the human ability to use information. The goal of this approach is to allow us to determine what tasks and stimuli our perceptual systems have evolved to deal with most efficiently. In this proposal, we describe a series of experiments designed to implement and test TBIOA with both simple and complex pattern discrimination tasks. In addition to the immediate goals of the two lines of research described above, there are also several eventual applications of this work. First, this research can be used to inform the design of applications that rely on efficient encoding of information, such as visual data transmission and compression as well as video surveillance technology. Second, this research could potentially be used to help understand how patients with disabilities related to visual perception (e.g., visual agnosias) differ from normal observers and inform how one might design human-machine interfaces to overcome such disabilities. PUBLIC HEALTH RELEVANCE The first goal of the research described in this proposal is to understand how well people use the information that is available to them when they perceive the bodily motions of their fellow humans. The second goal is to develop and test a new modeling approach called "task-based ideal observer analysis", which seeks to determine what tasks and stimuli the human visual system deals with most efficiently. This research has potentially important implications for applications related to data transmission and compression, video surveillance technology and for our eventual understanding and treatment of disorders related to the perception of visual motion (for example, visual agnosias in which people are unable to recognize particular classes of visual motion).